No abstract

Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data-classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber-optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least-squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis.

No abstract

No abstract

Raman microspectroscopic mapping enables one to study the chemical composition and molecular structure of subcellular components in individual cells without the need for labeling. Lung fibroblast cells were prepared under normal conditions and under stress, which was induced by 24 h of exposure to glyoxal. Raman microspectroscopic maps were recorded from fixed cells with 785-nm excitation and with 1-microm step width. Cluster analysis was applied to generate pseudocolor images of the cell morphology. Raman maps revealed that the cell nucleus shrinks in stressed cells, called pyknosis, which refers to an early stage of apoptosis. The intensity of nucleic acid bands decreased in cluster-averaged Raman spectra of the nucleus and cytoplasm, which is consistent with degradation and conformational changes of DNA and RNA. During a later stage of apoptosis, Raman maps indicate a rounding of cells, a further intensity decrease of nucleic acids bands, fragmentation of the nucleus, disappearance of lipid bodies, and formation of blisters at the cell surface. Whereas the peripheral membrane of the undisturbed cell is composed of lipids and cholesterol, the blisters have a higher protein content with nucleic acids incorporated. The results demonstrate that Raman spectroscopic mapping might become a powerful tool in cell biology for single cell analysis.

This study assessed the diagnostic potential of Raman spectroscopic mapping by evaluating its ability to distinguish between normal brain tissue and the human intracranial tumors gliomas and meningeomas. Seven Raman maps of native specimens were collected ex vivo by a Raman spectrometer with 785 nm excitation coupled to a microscope with a motorized stage. Variations within each Raman map were analyzed by cluster analysis. The dependence of tissue composition on the tissue type in cluster averaged Raman spectra was shown by linear combinations of reference spectra. Normal brain tissue was found to contain higher levels of lipids, intracranial tumors have more hemoglobin and lower lipid to protein ratios, meningeomas contain more collagen with maximum collagen content in normal meninges. One sample was studied without freezing. Whereas tumor regions did not change significantly, spectral changes were observed in the hemoglobin component after snap freezing and thawing to room temperature. The results constitute a basis for subsequent Raman studies to develop classification models for diagnosis of brain tissue.

We report for the first time a proof-of-concept experiment employing Raman spectroscopy to detect intracerebral tumors in vivo by brain surface mapping. Raman spectroscopy is a non-destructive biophotonic method which probes molecular vibrations. It provides a specific fingerprint of the biochemical composition and structure of tissue without using any labels. Here, the Raman system was coupled to a fiber-optic probe. Metastatic brain tumors were induced by injection of murine melanoma cells into the carotid artery of mice, which led to subcortical and cortical tumor growth within 14 days. Before data acquisition, the cortex was exposed by creating a bony window covered by a calcium fluoride window. Spectral contributions were assigned to proteins, lipids, blood, water, bone, and melanin. Based on the spectral information, Raman images enabled the localization of cortical and subcortical tumor cell aggregates with accuracy of roughly 250 μm. This study demonstrates the prospects of Raman spectroscopy as an intravital tool to detect cerebral pathologies and opens the field for biophotonic imaging of the living brain. Future investigations aim to reduce the exposure time from minutes to seconds and improve the lateral resolution.

No abstract